In this analysis I was interested in visualizing forestation loss, particularly in the Southern hemisphere. I downloaded a spreadsheet with annual forestation percentages by country and region from https://data.worldbank.org/indicator/AG.LND.FRST.ZS

I did some basic cleanup, including removing the 33 years of empty columns for most countries. I focused primarily on the change between 1990 and 2020. Thought I didn't use it in this analysis, I joined the descriptor files with the country data so I could group by region if desired.

region <- read.csv("CountryAbbrevDesc.csv")
countryRegion <- region[,1:2]

forest <- read.csv("ForestCountryPercentYear.csv")
#head(forest)

forest$loss30yr <- forest$X2020 - forest$X1990
#head(forest)  

forest30 <- forest[,c(1:2,35:66)]
#head(forest30)

forest30 <- forest30 %>%
  left_join(countryRegion, by="Country.Code")

#head(forest30)

#Set up data to be able to merge with map data
forest30$region <- forest30$Country.Name

The maps in this analysis display data by country where forest coverage percentage has been measured. Countries with no data will be gray on the final map.

country_map <- map_data("world") 

#merge map data to forestation data
country_forest <- map_data("world") %>%
  mutate(region = str_to_title(region),
         subregion = str_to_title(subregion)) %>% 
  left_join(forest30, by = "region")

#country_forest %>% head(n=30)

#formating to increase map size and decrease margins
options(repr.plot.width = 14, repr.plot.height = 14)
par(mar=c(1,1,1,1))

p_forest <- ggplot(data = country_forest,
            mapping = aes(x = long, y = lat, group = group, 
                          fill = loss30yr))

p_forest <- p_forest + geom_polygon(color="white")
p_forest <- p_forest + ggdendro::theme_dendro()
p_forest <- p_forest + scale_fill_viridis(option="viridis", direction=-1)
p_forest <- p_forest + guides(fill=guide_legend(title="Change in Forest Coverage Percentage 1990 - 2020"))
p_forest <- p_forest +  coord_map(xlim = c(-125,180),ylim = c(-50,35))
p_forest <- p_forest + ggtitle("Southern Hemisphere Forest Coverage Loss 1990-2020") + theme(plot.title = element_text(size=22)) + theme(legend.position="bottom")

#plot with legend
p_forest

#plot with tooltip for precision changes in percentage forestation
ggplotly(p_forest,tooltip = "loss30yr")

In this visualization, I was trying to communicate an idea of the the regions of the world most affected by deforestation. The colors closer to yellow show the greatest negative change in forest coverage while the colors closer to blue show an actual increase in forest coverage percentage. One aspect I found particularly interesting was how much more affected the southern hemisphere was. In my maps, I was getting errors with Alaska and Hawaii filling across a latitude line, so I finally cropped the map so it would not be distracting. However leaving the full map would have reinforced the degree to which South America and Africa are disproptionately affected.

Since the countries in the southern hemisphere start with a greater percentage forested, one futher analysis could be to look at the proportion of loss compared to the baseline year. If I was to continue working with this map, I would consider making the display show the country, zoom by continent, as well as showing a relationship between forestation and other environmental or public health factors.

I chose to have the tooltip displace the percentage change since the color scale wasn't as precise.